We will reanalyze data on risk factors for cardiovascular disease (CVD) including total cholesterol and HDL cholesterol for the subjects of the NHLBI twin study for examinations 1, 2, and 3 of the study. The analyses will utilize maximum likelihood estimators of genetic variance which are asymptotically more efficient than the method-of-moments estimators used in previous analyses. The models used will incorporate terms to partition the variance in a trait from twin data into either i) additive genetic variance and unshared environmental variance (the AE model), ii) additive genetic variance, dominance genetic variance, and unshared environmental variance (the ADE model), or iii) additive genetic variance, shared environmental variance, and unshared environmental variance (the ACE model). The AE, ADE, and ACE models can be fitted separately to data from each of the three exams to obtain a cross-sectional analysis. We shall also extend these models for use with longitudinal data by incorporating terms to represent the covariance of variance components from different exams. The results of these longitudinal analyses will yield new insights on genetic effects affecting CVD risk factors during the aging process. Two important additional objectives of this proposal are i) to introduce resistant estimation techniques in twin modeling, which trim the effect of outlier data points smoothly, and ii) to carefully study the performance of maximum likelihood and method-of-moments estimators when assumptions of the twin model are violated. The results of these parts of the proposal will yield a more complete understanding of the relative merits and limitations of twin modeling procedures.